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    Wyświetlanie 1-7 z 7
    Tytuł:
    Projective nonnegative matrix factorization based on α-divergence
    Autorzy:
    Yang, Z.
    Oja, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/91672.pdf
    Data publikacji:
    2011
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    Nonnegative Matrix Factorization
    NMF
    α-divergence
    PNMF
    α-NMF
    α-PNMF
    Opis:
    The well-known Nonnegative Matrix Factorization (NMF) method can be provided with more flexibility by generalizing the non-normalized Kullback-Leibler divergence to α- divergences. However, the resulting α-NMF method can only achieve mediocre sparsity for the factorizing matrices. We have earlier proposed a variant of NMF, called Projective NMF (PNMF) that has been shown to have superior sparsity over standard NMF. Here we propose to incorporate both merits of α-NMF and PNMF. Our α-PNMF method can produce a much sparser factorizing matrix, which is desired in many scenarios. Theoretically, we provide a rigorous convergence proof that the iterative updates of α-PNMF monotonically decrease the α-divergence between the input matrix and its approximate. Empirically, the advantages of α-PNMF are verified in two application scenarios: (1) it is able to learn highly sparse and localized part-based representations of facial images; (2) it outperforms α-NMF and PNMF for clustering in terms of higher purity and smaller entropy.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 1; 7-16
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multiplicative Algorithm for Correntropy-Based Nonnegative Matrix Factorization
    Autorzy:
    Hosseini-Asl, E.
    Zurada, J. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/108758.pdf
    Data publikacji:
    2013
    Wydawca:
    Społeczna Akademia Nauk w Łodzi
    Tematy:
    Nonnegative Matrix Factorization (NMF)
    Correntropy
    Multiplicative Algorithm
    Document Clustering
    Opis:
    Nonnegative matrix factorization (NMF) is a popular dimension reduction technique used for clustering by extracting latent features from highdimensional data and is widely used for text mining. Several optimization algorithms have been developed for NMF with different cost functions. In this paper we evaluate the correntropy similarity cost function. Correntropy is a nonlinear localized similarity measure which measures the similarity between two random variables using entropy-based criterion, and is especially robust to outliers. Some algorithms based on gradient descent have been used for correntropy cost function, but their convergence is highly dependent on proper initialization and step size and other parameter selection. The proposed general multiplicative factorization algorithm uses the gradient descent algorithm with adaptive step size to maximize the correntropy similarity between the data matrix and its factorization. After devising the algorithm, its performance has been evaluated for document clustering. Results were compared with constrained gradient descent method using steepest descent and L-BFGS methods. The simulations show that the performance of steepest descent and LBFGS convergence are highly dependent on gradient descent step size which depends on σ parameter of correntropy cost function. However, the multiplicative algorithm is shown to be less sensitive to σ parameterand yields better clustering results than other algorithms. The results demonstrate that clustering performance measured by entropy and purity improve the clustering. The multiplicative correntropy-based algorithm also shows less variation in accuracy of document clusters for variable number of clusters. The convergence of each algorithm is also investigated, and the experiments have shown that the multiplicative algorithm converges faster than L-BFGS and steepest descent method.
    Źródło:
    Journal of Applied Computer Science Methods; 2013, 5 No. 2; 89-104
    1689-9636
    Pojawia się w:
    Journal of Applied Computer Science Methods
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Model-Based Method for Acoustic Echo Cancelation and Near-End Speaker Extraction: Non-negative Matrix Factorization
    Autorzy:
    Agrawal, P.
    Shandilya, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/958086.pdf
    Data publikacji:
    2018
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    adaptive algorithms
    convergence
    echo cancellation
    non-negative matrix factorization (NMF)
    Opis:
    Rapid escalation of wireless communication and hands-free telephony creates a problem with acoustic echo in full-duplex communication applications. In this paper a simulation of model-based acoustic echo cancelation and near-end speaker extraction using statistical methods relying on nonnegative matrix factorization (NMF) is proposed. Acoustic echo cancelation using the NMF algorithm is developed and its implementation is presented, along with all positive, real time elements and factorization techniques. Experimental results are compared against the widely used existing adaptive algorithms which have a disadvantage in terms of long impulse response, increased computational load and wrong convergence due to change in near-end enclosure. All these shortcomings have been eliminated in the statistical method of NMF that reduces echo and enhances audio signal processing.
    Źródło:
    Journal of Telecommunications and Information Technology; 2018, 2; 15-22
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Frequency Selection Based Separation of Speech Signals with Reduced Computational Time Using Sparse NMF
    Autorzy:
    Varshney, Y. V.
    Abbasi, Z. A.
    Abidi, M. R.
    Farooq, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/176829.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    sparse NMF
    non-negative matrix factorisation
    mixed speech recognition
    machine learning
    Opis:
    Application of wavelet decomposition is described to speed up the mixed speech signal separation with the help of non-negative matrix factorisation (NMF). It is assumed that the basis vectors of training data of individual speakers had been recorded. In this paper, the spectrogram magnitude of a mixed signal has been factorised with the help of NMF with consideration of sparseness of speech signals. The high frequency components of signal contain very small amount of signal energy. By rejecting the high frequency components, the size of input signal is reduced, which reduces the computational time of matrix factorisation. The signal of lower energy has been separated by using wavelet decomposition. The present work is done for wideband microphone speech signal and standard audio signal from digital video equipment. This shows an improvement in the separation capability using the proposed model as compared with an existing one in terms of correlation between separated and original signals. Obtained signal to distortion ratio (SDR) and signal to interference ratio (SIR) are also larger as compare of the existing model. The proposed model also shows a reduction in computational time, which results in faster operation.
    Źródło:
    Archives of Acoustics; 2017, 42, 2; 287-295
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks
    Autorzy:
    Jeevangi, Sanjeevkumar
    Jawaligi, Shivkumar
    Patil, Vilaskumar
    Powiązania:
    https://bibliotekanauki.pl/articles/2174451.pdf
    Data publikacji:
    2022
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    cognitive radio
    improved NMF
    LU-SLNO system
    optimized CNN
    spectrum sensing
    Opis:
    Vacant frequency bands are used in cognitive radio (CR) by incorporating the spectrum sensing (SS) technique. Spectrum sharing plays a central role in ensuring the effectiveness of CR applications. Therefore, a new multi-stage detector for robust signal and spectrum sensing applications is introduced here. Initially, the sampled signal is subjected to SNR estimation by using a convolutional neural network (CNN). Next, the detection strategy is selected in accordance with the predicted SNR levels of the received signal. Energy detector (ED) and singular value-based detector (SVD) are the solutions utilized in the event of high SNR, whilst refined non-negative matrix factorization (MNMF) is employed in the case of low SNR. CNN weights are chosen via the Levy updated sea lion optimization (LU-SLNO) algorithm inspired by the traditional sea lion optimization (SLNO) approach. Finally, the outcomes of the selected detectors are added, offering a precise decision on spectrum tenancy and existence of the signal.
    Źródło:
    Journal of Telecommunications and Information Technology; 2022, 4; 21--32
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Unsupervised dynamic topic model for extracting adverse drug reaction from health forums
    Autorzy:
    Eslami, Behnaz
    Motlagh, Mehdi Habibzadeh
    Rezaei, Zahra
    Eslami, Mohammad
    Amini, Mohammad Amin
    Powiązania:
    https://bibliotekanauki.pl/articles/117691.pdf
    Data publikacji:
    2020
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    Deep Learning
    topic modeling
    Text Mining
    ADR
    NMF
    analiza tekstu
    uczenie maszynowe
    modelowanie tematyczne
    Opis:
    The relationship between drug and its side effects has been outlined in two websites: Sider and WebMD. The aim of this study was to find the association between drug and its side effects. We compared the reports of typical users of a web site called: “Ask a patient” website with reported drug side effects in reference sites such as Sider and WebMD. In addition, the typical users’ comments on highly-commented drugs (Neurotic drugs, Anti-Pregnancy drugs and Gastrointestinal drugs) were analyzed, using deep learning method. To this end, typical users’ comments on drugs' side effects, during last decades, were collected from the website “Ask a patient”. Then, the data on drugs were classified based on deep learning model (HAN) and the drugs’ side effect. And the main topics of side effects for each group of drugs were identified and reported, through Sider and WebMD websites. Our model demonstrates its ability to accurately describe and label side effects in a temporal text corpus by a deep learning classifier which is shown to be an effective method to precisely discover the association between drugs and their side effects. Moreover, this model has the capability to immediately locate information in reference sites to recognize the side effect of new drugs, applicable for drug companies. This study suggests that the sensitivity of internet users and the diverse scientific findings are for the benefit of distinct detection of adverse effects of drugs, and deep learning would facilitate it.
    Źródło:
    Applied Computer Science; 2020, 16, 1; 41-59
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Regularized nonnegative matrix factorization: Geometrical interpretation and application to spectral unmixing
    Autorzy:
    Zdunek, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/329732.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    blind source separation
    nonnegative matrix factorization
    active set algorithm
    regularized NMF
    polytope approximation
    ślepa separacja sygnału
    nieujemna faktoryzacja macierzy
    Opis:
    Nonnegative Matrix Factorization (NMF) is an important tool in data spectral analysis. However, when a mixing matrix or sources are not sufficiently sparse, NMF of an observation matrix is not unique. Many numerical optimization algorithms, which assure fast convergence for specific problems, may easily get stuck into unfavorable local minima of an objective function, resulting in very low performance. In this paper, we discuss the Tikhonov regularized version of the Fast Combinatorial NonNegative Least Squares (FC-NNLS) algorithm (proposed by Benthem and Keenan in 2004), where the regularization parameter starts from a large value and decreases gradually with iterations. A geometrical analysis and justification of this approach are presented. The numerical experiments, carried out for various benchmarks of spectral signals, demonstrate that this kind of regularization, when applied to the FC-NNLS algorithm, is essential to obtain good performance.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 2; 233-247
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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